Science at ALCF

A visualization of 1 percent of the neurons in a digital reconstruction and simulation of the neocortex. Synapses, the processes that mediate the connections between neurons, can change their efficacy, new synapses can form, and existing ones can disappear, driven by network activity. These activity-dependent modifications, collectively known as synaptic plasticity, are thought to be the substrate of learning and memory.Nicolas Antille, Blue Brain, EPFL

Biophysical Principles of Functional Synaptic Plasticity in the Neocortex

PI Name:

Eilif Muller

Institution:

Blue Brain, EPFL

Allocation Program:

INCITE

Allocation Hours at ALCF:

100 million

Year:

2017

Research Domain:

Biological Sciences

During our lifetimes, our brains undergo continuous changes as a consequence of our experiences. Synaptic plasticity—the biological process by which brain activity leads to changes in synaptic connections—is thought to be central to learning and memory. However, little is known about how this process shapes the specialization of biological neural networks.

For this INCITE project, researchers from École Polytechnique Fédérale de Lausanne will use Mira to advance the understanding of these fundamental mechanisms of the brain’s neocortex. The team will carry out large-scale simulations of recently uncovered biophysical principles underlying synaptic plasticity in reconstructions of a neocortical microcircuit (Markram et al., 2015; 10.1016/j.cell.2015.09.029) consisting of around 200,000 neurons and 260 million synapses. The aim is to shed light on the synergistic functional principles that shape plasticity in realistic cortical circuits. To support these computationally intensive simulations, they will pursue several key technological developments, including job execution optimizations, and the implementation of an efficient and scalable checkpointing/restart strategy.

The team is targeting three scientific milestones: (1) characterizing the role of NMDA receptor spikes in plasticity induction; (2) characterizing the dynamics of neuronal assembly formation and maintenance; and (3) characterizing the computational impact of synaptic plasticity in common signal processing tasks. In addition to improving our understanding of the brain, this research could help inform the development of more optimized deep learning methods, as well as new learning paradigms for neuromorphic hardware.